School of Software, Central South University, Changsha 410075, China.
Molecules. 2018 Oct 4;23(10):2535. doi: 10.3390/molecules23102535.
Hot spots are the subset of interface residues that account for most of the binding free energy, and they play essential roles in the stability of protein binding. Effectively identifying which specific interface residues of protein⁻protein complexes form the hot spots is critical for understanding the principles of protein interactions, and it has broad application prospects in protein design and drug development. Experimental methods like alanine scanning mutagenesis are labor-intensive and time-consuming. At present, the experimentally measured hot spots are very limited. Hence, the use of computational approaches to predicting hot spots is becoming increasingly important. Here, we describe the basic concepts and recent advances of machine learning applications in inferring the protein⁻protein interaction hot spots, and assess the performance of widely used features, machine learning algorithms, and existing state-of-the-art approaches. We also discuss the challenges and future directions in the prediction of hot spots.
热点是指在蛋白质结合中起关键作用的界面残基子集，它们在蛋白质结合的稳定性中起着至关重要的作用。有效地识别蛋白质-蛋白质复合物中哪些特定的界面残基形成热点对于理解蛋白质相互作用的原理具有重要意义，并且在蛋白质设计和药物开发中有广泛的应用前景。像丙氨酸扫描突变这样的实验方法既费时又费力。目前，实验测量的热点非常有限。因此，使用计算方法来预测热点变得越来越重要。在这里，我们描述了机器学习在推断蛋白质-蛋白质相互作用热点中的应用的基本概念和最新进展，并评估了广泛使用的特征、机器学习算法和现有最先进方法的性能。我们还讨论了热点预测中的挑战和未来方向。
